Triple

T20559381
Position Surface form Disambiguated ID Type / Status
Subject Agfa film production E504805 entity
Predicate operatedBy P86 FINISHED
Object Agfa AG NE NERFINISHED

How this triple was built (2 steps)

Every LLM step that produced this triple, in pipeline order — named-entity classification, the disambiguation choices (the exact options shown, with the pick highlighted), and the generated description. The batch + timestamp of each is in the Provenance table below.

NER Named-entity recognition gpt-5-mini
Instruction
Given a phrase, classify it is english named entity (e.g., persons, organizations, works of art) in Latin script, or not (e.g., literals, dates, URLs, verbose phrases). For disambiguation, the statement where the phrase occurs as object is also given. Please return a JSON object with `phrase` (string, the phrase being analyzed) and `is_ne` (boolean, indicating whether the phrase is a Named Entity).
Input
Phrase: Agfa AG | Statement: [Agfa film production, operatedBy, Agfa AG]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Agfa AG
Context triple: [Agfa film production, operatedBy, Agfa AG]
  • A. Agfa chosen
    Agfa is a historic German company best known for its photographic films, cameras, and imaging technologies.
  • B. Aral AG
    Aral AG is a major German brand of fuel stations and petroleum products, widely recognized for its network of service stations across Germany.
  • C. MacDermid
    MacDermid is a surname of Scottish origin, commonly regarded as a variant spelling of McDiarmid.
  • D. Hechtel-Eksel
    Hechtel-Eksel is a municipality in the Belgian province of Limburg, known for its extensive forests, nature reserves, and role as a gateway to the Hoge Kempen and Bosland natural areas.
  • E. Xerox
    Xerox is an American corporation best known for pioneering photocopiers and influential computing innovations, including early graphical user interfaces and office software.
  • F. None of above.
  • G. Unsure - the case is ambiguous/there is not enough information to decide.

Provenance (2 batches)

The batch behind each pipeline step, in order, with when it ran. Timestamps are batch-level — stages were processed in waves, so the object chain (NER → NED1 → NEDg → NED2) reads in order, but predicate / elicitation batches can sit in a different wave.

Step Stage Batch ID Status When
creating Elicitation batch_69e0b4b6587c8190aee63dc7cff244ea completed April 16, 2026, 10:06 a.m.
NER Named-entity recognition batch_69e6a5e178648190910795bae5422e50 completed April 20, 2026, 10:17 p.m.
Created at: April 16, 2026, 11:38 a.m.